The video super-resolution (VSR) method based on the recurrent convolutional network has strong temporal modeling capability for video sequences. However, the temporal receptive field of different recurrent units in the unidirectional recurrent network is unbalanced. Earlier reconstruction frames receive less spatio-temporal information, resulting in fuzziness or artifacts. Although the bidirectional recurrent network can alleviate this problem, it requires more memory space and fails to perform many tasks with low latency requirements. To solve the above problems, we propose an end-to-end information prebuilt recurrent reconstruction network (IPRRN), consisting of an information prebuilt network (IPNet) and a recurrent reconstruction network (RRNet). By integrating sufficient information from the front of the video to build the hidden state needed for the initially recurrent unit to help restore the earlier frames, the information prebuilt network balances the input information difference at different time steps. In addition, we demonstrate an efficient recurrent reconstruction network, which outperforms the existing unidirectional recurrent schemes in all aspects. Many experiments have verified the effectiveness of the network we propose, which can effectively achieve better quantitative and qualitative evaluation performance compared to the existing state-of-the-art methods.

翻译：基于循环卷积网络的视频超分辨率（VSR）方法对视频序列具有较强的时间建模能力。然而，单向循环网络中不同循环单元的时间感受野并不均衡。较早的重建帧接收到的时空信息较少，从而产生模糊或伪影。尽管双向循环网络可以缓解这一问题，但它需要更多内存空间，并且无法执行许多对低延迟有要求的任务。为解决上述问题，我们提出了一种端到端的信息预建循环重建网络（IPRRN），由信息预建网络（IPNet）和循环重建网络（RRNet）组成。通过整合视频前端的充足信息来构建初始循环单元所需的隐藏状态，以帮助恢复较早帧，信息预建网络平衡了不同时间步的输入信息差异。此外，我们展示了一种高效的循环重建网络，它在各方面均优于现有的单向循环方案。大量实验验证了我们所提出网络的有效性，与现有最先进方法相比，能够有效实现更优的定量和定性评估性能。